Internet searching has become increasingly common in recent years. In many cases, a user's first attempt at performing a search query does not yield desired results. Typically, the user then reformulates the search query in an attempt to return the desired results. The problem of a user's entered query not identifying the results the user is seeking is known as the “query-document vocabulary mismatch.” Conventional search engines may attempt to solve this problem by automatically reformulating queries or suggesting replacements for particular terms in the search query. For example, when a user's search includes the term “bio,” the search engine may also search using the term “biography.” Search engines conventionally rely on user data such as lists of frequently submitted queries, human-created or human-annotated lists such as thesauri, and other sources. Human-created and human-annotated lists in particular include inherent biases. Additionally, while pairs of query terms and replacement terms in these lists may seem appropriate to a human reviewer, the replacement terms will not necessarily solve the query-document vocabulary mismatch and cause the search engine to return more relevant documents.